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Author Topic: The 2017/2018 freezing season  (Read 24220 times)


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Re: The 2017/2018 freezing season
« Reply #200 on: October 16, 2017, 05:58:11 PM »
According to the weather forecasts no significant coldness will come or even things may get warmer
Here is the ESRL forecast out to Oct 24th for 80ºN. Forty 2m air temperatures are provided at 6hr intervals and the average determined. The identical result is displayed in a variety of color tables with a scale that runs from -22º to +6º C. These variations illustrate how interpretations can be helped or hindered by presentation choices.

 Warmer air appears to be intruding well into the interior from the North Atlantic though the CAA remains cold. However it is not warm enough to melt any snow on ice. This time of year, snow retards bottom ice formation by insulating the top ice from air.

The second animation shows these temperatures for the Arctic Ocean as a whole over the same time frame. This shows air flow well but it is not easy to get a sense of the time-averaged temperature from it. The averaged whole ocean temperature has a red line indicating the southern boundary of sea water above its freezing temperature of -1.8ºC

Technical note: Panoply was run in linear grayscale mode on The 40 frames are then averaged to a single grayscale in Gimp. All extraneous pixels are removed, leaving only the image plus its palette as 256 grays. Lookup tables in ImageJ are applied, those that seem informative are saved as .png, reloaded as an ImageJ stack, and saved out as a gif. Gimp has a bad bug in gifs that causes it to seek a global color table whereas gif89 allows each frame to have its own color table. The cluts used here are gray, glow, redHot, ICA3, physics, royal, rainbow, rire, cool, and inverted glasbey with the addition of G'mic contouring in some instances.
« Last Edit: October 16, 2017, 09:09:18 PM by A-Team »


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Re: The 2017/2018 freezing season
« Reply #201 on: Today at 09:46:40 AM »
It's that time of year again, when thousands of abstracts for AGU meeting become available. Hardly anyone discloses results, posters aren't available for poster sessions, talks won't be videoed, their powerpoints won't be archived, and already-published articles won't be linked.

Still, AGU17 does allow a look ahead to the coming year of journal articles. A name search can show what a particular scientist has been up to; for example Neven asked upforum what J Stroeve is doing, she is on three of the abstracts.

Snow on Arctic sea ice is an active topic. Like ice thickness and clouds, it is very difficult to characterize basin-wide, in part because depth alone doesn't capture its insulating properties in freeze season: it's blown into windrows, it may be dunked in sea water on a floe with negative freeboard, or be rained upon and refreeze. Still, it looks like some better products than what we have now may be in the pipeline.

C23E-08: Merging observations and reanalysis data to improve estimates of snow depth on Arctic sea ice
NT Kurtz et al

Snow is an important controlling factor in the heat and radiation balance of the Arctic sea ice pack. Knowledge of snow on sea ice is also required for retrievals of sea ice thickness from airborne and spaceborne altimeters, and is presently the largest source of uncertainty in the conversion of freeboard to sea ice thickness from these altimetry data.

Multiple sources of observational snow depth data exist such as those from the Operation IceBridge (OIB) snow radar, passive microwave satellites, and ice mass balance buoys. However, these observational data sources are limited in spatial and/or temporal extent, which makes their usage impractical when used for basin-wide sea ice thickness retrievals in a standalone fashion.

We show how the use of snow depth observations from the OIB snow radar can be used as a primary means to improve basin-scale snow depth results from a simple snow model forced by reanalyses and satellite-derived ice drift estimates. We also show how different observational data sets impact the snow depth estimates, and how best to incorporate data sets of differing temporal and spatial scales to provide snow thickness estimates of consistent quality over the entire sea ice growth season. Particular focus is given to the new 2017 OIB data set which included new flights into the eastern Arctic sector where interesting differences were seen between the first year and multiyear ice areas.

C32B-02: Snow accumulation on Arctic sea ice: is it a matter of how much or when?
M Webster  et al

Snow on sea ice plays an important, yet sometimes opposing role in sea ice mass balance depending on the season. In autumn and winter, snow reduces the heat exchange from the ocean to the atmosphere, reducing sea ice growth. In spring and summer, snow shields sea ice from solar radiation, delaying sea ice surface melt. Changes in snow depth and distribution in any season therefore directly affect the mass balance of Arctic sea ice.

In the western Arctic, a decreasing trend in spring snow depth distribution has been observed and attributed to the combined effect of peak snowfall rates in autumn and the coincident delay in sea ice freeze-up. Here, we present an in-depth analysis on the relationship between snow accumulation and the timing of sea ice freeze-up across all Arctic regions. A newly developed two-layer snow model is forced with eight reanalysis precipitation products to: (1) identify the seasonal distribution of snowfall accumulation for different regions, (2) highlight which regions are most sensitive to the timing of sea ice freeze-up with regard to snow accumulation, and (3) show, if precipitation were to increase, which regions would be most susceptible to thicker snow covers. We also utilize a comprehensive sensitivity study to better understand the factors most important in controlling winter/spring snow depths, and to explore what could happen to snow depth on sea ice in a warming Arctic climate.

C33C-1215: Rainy Days in the New Arctic: A Comprehensive Look at Precipitation from 8 Reanalysis
L Boisvert  et al

Precipitation in the Arctic plays an important role in the fresh water budget, and is the primary control of snow accumulation on sea ice. However, Arctic precipitation from reanalysis is highly uncertain due to differences in the atmospheric physics and use of data assimilation and sea ice concentrations across the different products. More specifically, yearly cumulative precipitation in some regions can vary by 100-150 mm across reanalyses. This creates problems for those modeling snow depth on sea ice, specifically for use in deriving sea ice thickness from satellite altimetry.

In recent years, this new Arctic has become warmer and wetter, and evaporation from the ice-free ocean has been increasing, which leads to the question: is more precipitation falling and is more of this precipitation rain? This could pose a big problem for model and remote sensing applications and studies those modeling snow accumulation because rain events will can melt the existing snow pack, reduce surface albedo, and modify the ocean-to-atmosphere heat flux via snow densification.

In this work we compare precipitation (both snow and rain) from 8 different reanalysis: MERRA, MERRA2, NCEP-R1, NCEP-R2, ERA-Interim, ERA-5, ASR and JRA-55. We examine the annual, seasonal, and regional differences and compare with buoy data to assess discrepancies between products during observed snowfall and rainfall events. Magnitudes and frequencies of these precipitation events are evaluated, as well as the “residual drizzle” between reanalyzes. Lastly, we will look at whether the frequency and magnitude of “rainy days” in the Arctic have been changing over recent decades.

C21B-1122: Synoptic weather conditions, clouds, and sea ice in the Beaufort and Chukchi Seasonal Ice Zone
Z Liu et al

The connections between synoptic conditions and clouds and sea ice over the Beaufort and Chukchi Seasonal Ice Zone are examined. Four synoptic states with distinct thermodynamic and dynamic spatial and vertical signatures are identified using a k-means classification algorithm and the ERA-Interim reanalysis data from 1979 to 2014.

The combined CloudSat and Calipso cloud observations suggest control of clouds by synoptic states. Warm continental air advection is associated with the fewest low-level clouds, cold air advection under low pressure generates the most low-level clouds. Low-level cloud fractions are related to lower-tropospheric stability and both are regulated by synoptic conditions. Observed cloud vertical and spatial variability is reproduced well in ERA-Interim, but winter low-level cloud fraction is overestimated.

Sea ice melt onset is related to synoptic conditions. Melt onsets occur more frequently and earlier with warm air advection states. The warm continental air advection state with the highest temperature is the most favorable for melt onsets even though fewer low-level clouds are associated with this state. The other warm advection state is cloudier but colder. In the Beaufort and Chukchi Seasonal Ice Zone, the much higher temperature and total column water of the warm continental air advection state compensate the smaller cloud longwave radiative fluxes due to the smaller low-level cloud fraction. In addition, the higher shortwave radiative fluxes and turbulent fluxes to the surface are also favorable for sea ice melt onset.

C21G-1186: There goes the sea ice: following Arctic sea ice parcels and their properties.
MA Tschudi et al

Arctic sea ice distribution has changed considerably over the last couple of decades. Sea ice extent record minimums have been observed in recent years, the distribution of ice age now heavily favors younger ice, and sea ice is likely thinning. This new state of the Arctic sea ice cover has several impacts, including effects on marine life, feedback on the warming of the ocean and atmosphere, and on the future evolution of the ice pack.

The shift in the state of the ice cover, from a pack dominated by older ice, to the current state of a pack with mostly young ice, impacts specific properties of the ice pack, and consequently the pack’s response to the changing Arctic climate. For example, younger ice typically contains more numerous melt ponds during the melt season, resulting in a lower albedo. First-year ice is typically thinner and more fragile than multi-year ice, making it more susceptible to dynamic and thermodynamic forcing.

To investigate the response of the ice pack to climate forcing during summertime melt, we have developed a database that tracks individual Arctic sea ice parcels along with associated properties as these parcels advect during the summer. Our database tracks parcels in the Beaufort Sea, from 1985 – present, along with variables such as ice surface temperature, albedo, ice concentration, and convergence.

We are using this database to deduce how these thousands of tracked parcels fare during summer melt, i.e. what fraction of the parcels advect through the Beaufort, and what fraction melts out? The tracked variables describe the thermodynamic and dynamic forcing on these parcels during their journey. The attached image (it’s not) shows the ice surface temperature of all parcels (right) that advected through the Beaufort Sea region (left) in 2014.

C33C-1210: Towards development of an operational snow-on-sea-ice product
GE Liston et al

While changes in the spatial extent of sea ice have been routinely monitored since the 1970s, less is known about how the thickness of the ice cover has changed. While estimates of ice thickness across the Arctic Ocean have become available over the past 20 years based on data from ERS-1/2, Envisat, ICESat, CryoSat-2 satellites and Operation IceBridge aircraft campaigns, the variety of these different measurement approaches, sensor technologies and spatial coverage present formidable challenges. Key among these is that measurement techniques do not measure ice thickness directly – retrievals also require snow depth and density.

Towards that end, a sophisticated snow accumulation model is tested in a Lagrangian framework to map daily snow depths across the Arctic sea ice cover using atmospheric reanalysis data as input. Accuracy of the snow accumulation is assessed through comparison with Operation IceBridge data and ice mass balance buoys (IMBs). Impacts on ice thickness retrievals are further discussed.
« Last Edit: Today at 10:53:28 AM by A-Team »